Estimating the intention of space objects plays an important role in aircraft design, aviation safety, military and other fields, and is an important reference basis for air situation analysis and command decision-making. This paper studies an intention estimation method based on fuzzy theory, combining probability to calculate the intention between two objects. This method takes a space object as the origin of coordinates, observes the target's distance, speed, relative heading angle, altitude difference, steering trend and etc., then introduces the specific calculation methods of these parameters. Through calculation, values are input into the fuzzy inference model, and finally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability. Verified by simulation experiment, the target intention inferred by this method is roughly the same as the actual behavior of the target, which proves that the method for identifying the target intention is effective.
DEAH box protein DHX33 has been found to be necessary for cell proliferation and early development of multicellular organisms. It plays diverse roles in regulating gene transcription, ribosome RNA synthesis, and protein translation. Dysregulation of DHX33 has been observed in various human cancers. In this study, we identified a short DHX33 variant in cells. The short DHX33 (hereafter referred to as DHX33‐2) has only 534 amino acids, which completely matches the C‐terminal helicase domain of full‐length DHX33 (DHX33‐1). Different from DHX33‐1, which mainly localizes to the nucleus, DHX33‐2 preferentially localizes to the cytoplasm. Through protein immunoprecipitation and RNA‐ immunoprecipitation analysis, we found that DHX33‐2 interacts with DDX3, eIF3, hnRNPs, poly (A) binding protein, and a subset of mRNAs. Further RNA sequencing analysis showed that DHX33 binds to a subset of mRNAs important in cell proliferation. DHX33‐2 stimulates the translation for specific mRNAs. Our study for the first time demonstrates the function of a short DHX33 variant in protein translation.
As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed based on the random forest classification method with a new combination of features and it obtains better prediction results. Simultaneously, it further shows the degree of feature parameters influence on GFA. Finally, a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first time. The result shows that the application of machine learning in MGs is valuable.
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